How do you actually obtain and report a likelihood function for scientific research?
I just read the article https://arbital.com/p/likelihoods_not_pvalues/, but this left me with one big question: what is the actual sequence of actions a researcher would take to report a likelihood function based on their research? For background, I’m an undergraduate student who has participated in multiple undergraduate research positions, and who has so far been uncertain about how to compute and use likelihood functions based on the data (mostly econometric data) involved in this research.
In particular, I want to find out:
How do you compute a likelihood function in Stata, R, Python, etc. based on data you have, for a given hypothesis class
How do you obtain a data file for this likelihood function that can be distributed in a way that other researchers can download and use in e.g. a meta-analysis
What kinds of graphs are good to visually represent these likelihood functions, particularly if the hypothesis class has multiple parameters, and
What kinds of summary statistics are good to numerically represent these likelihood functions in a human-readable way
How has this whole thing historically been done in fields like machine learning that have historically been more amenable to reporting likelihood functions
If you have good answers for any of these questions, I’d really like to know.